Im a 17 year old guy who is in no way an expert on ai/coding etc. I just know a tiny bit of basic coding taught to us in school but am severely interested in ai and technological development and have been naturally pro-ai .

The past few weeks , I just can not wrap my head around what the actual heck is even happening. None of it seems real. Just how damn good the models have gotten seemingly suddenly, hugging face incident , openai solving fucking millenium prize problems and publishing 700 proofs(?) in a day , ai reverse engineering, and now the php incident.

I was just watching the tesla roadster showcase the other day and was losing my mind not being able to tell if it was real or a fake ai channel. It literally felt like I was on drugs(never been close to any) . How is everyone irl so calm and dandy and just loving their life? Yet I go on the internet and it's a literal revolution everyday . Am I the one overreacting and going crazy? What is even happening anymore and where are we headed? Can someone please confirm if any of it is even real ?

I don't know what the future or the present for that matter holds for youngsters like me but I'm excited(?) . I have always been a lost person but now I guess everyone is.

This report treats embodied competence as a question of generalization and practical agency, not just task success. Across 12 benchmarks, GPT-6-Astra reaches strong results when given in-context demonstrations and much larger action budgets; for example, ICL raises success on four shared SIMPLE tasks from 70.8% to 90.8%. That is meaningful evidence of broad task adaptation, but it is not zero-shot generality: performance is more moderate where no in-domain demonstrations are available.

The gap between benchmark competence and practical embodied intelligence is visible in both costs and failure modes. Some extended episodes consume millions of tokens and take hours, while physical execution cannot simply trade more time for more attempts. Extra actions can also compound errors or undo completed progress, and real-world plug insertion fails in all 11 reported trials.

The paper’s proposed evaluation direction—no in-domain demonstrations and a world clock that keeps running during deliberation—seems important for testing whether an agent can act reliably under changing conditions, rather than exploit a generous interaction budget. The results support a qualified claim: broad embodied competence is emerging, but reliable, efficient general-purpose physical agency remains a much higher bar.

Full summary on AIModels.fyi

Original paper

Disclosure: AIModels.fyi is my site.

List of incidents:

1. PHP2Shell: a hole in the software behind ~70% of websites. Governments took sites offline.

2. eMudhra: the master keys that prove websites are real were cracked. One was the number 4.

3. California: data on every public school student since 2023 stolen, including disability status

4. Railway: hackers stole customers' secret keys from a hosting platform used by millions of developers

5. Zeabur: 321,810 accounts breached. The company is shutting down.

6. LiteLLM: an AI tool downloaded 3.4 million times a day was poisoned with a password stealer

7. GitLab: a 10-out-of-10 severity hole in every version since 2014

8. Cairn: criminals used an AI hacking tool to break into 100+ companies

9. Ahold Delhaize: hackers knocked the owner of Food Lion and Stop & Shop offline

10. Onamae: malware planted on up to 26,739 customer servers in Japan

11. Signal: a bug could leave your mic and camera on after you hang up

12. NGINX: 3 critical holes in software running ~1/3 of websites

13. HTTP/3: a single connection can crash 7 of the 10 web server types tested

14. Plex: hackers breaking into home media servers

15. SafePay ransomware: FBI warns it has hit 600+ critical infrastructure organizations

16. Palo Alto: ransomware gangs breaking in through company firewalls

17. Bullhorn: platform used by 10,000+ companies knocked offline

18. Medi-Cal: 970,000 patients' data stolen

19. WordPress: hackers stole a plugin maker's keys and used them to plant backdoors on websites

20. AOMEI: backup software's update server handed out a backdoor

21. Meta Muse: Meta's AI agent exposed users' personal and business secrets

22. Tor: 333 relays hijacked to steal logins

23. TiDB Cloud: database company's cloud breached

24. State Bank of India: India's biggest bank confirms a data breach

25. OVHcloud: hackers slipped a password stealer into a cloud company's official tool

26. Siemens: a software hole being used to spread malware

27. North Korea: state hackers launch a new attack campaign on South Korea

Let's state it. AGI is even more likely to happen in 2-24 months than ever. But we know this has a price, in electronics, in compute.

So it makes sense to determine what would be the first tasks AGI should solve, other than the obvious global warming.

My 2 cents:

A) Energy: Low impact easy energy generators, either by classical fusion, laser-boron, etc. more efficient solar panels, other forms of energy to be harvested.

B) Electronics: manufacturing and technologies to overcome shortages and be less power-hungry

once AGI itself is sustainable and cheap:

C) Medical conditions and longevity

D) Energy storation: batteries.

E) Wealth for everyone: in a world with real abundance, no meaning to have wealth/social disparities

And for you what would be the priority?

2027 is the year of AI-generated video that is indistinguishable from real footage.

2027 is the year an open-weight model is released that can run on an M-series chip.

2027 is the year artists have a resurgence against slop, and people finally see their worth.

2027 is the year America bans open-weight AI models.

2027 is the year China releases an open-weight model that supersedes anything to date (Reddit and X will confirm this one); benchmarks will say otherwise.

2027 is the year the general public learns a new context for the word “distilling.”

2027 is the year we hear Bill Gates tell us, “I warned you.”

2027 is the year we wish we could turn it all back.

TLDR: A non-mathematician (me) can solve unsolved problems with Claude. I provide the proofs and a template for you to work with Claude on your own problems. I can't wait to see what you find!

Anyone interested in this has likely seen that OpenAI has recently increased the lower bound of χ(R\^2) from 5 to 6.

Before that happened though, I was working with Claude on the same question.

We have many proofs of tangential open questions, the most asked being "What is the chromatic number of the plane over the algebraic number field Q(sqrt(47))". We prove that it is 5.

The most interesting to me is that the planes of some number fields have a chromatic number equal to that of the plane over the reals.

One such plane is the field that de Grey's original 5 chromatic graph lies on: Q(sqrt(3), sqrt(5), sqrt(7), sqrt(11)), and also Q(sqrt(635087)).

All details, including Lean formalization and the workflow that led to the findings, are in the repo here: https://github.com/kooshi/hadwiger-nelson

I'm not an academic or a mathematician, just a software developer who likes working with Claude on hard problems and coming up with workflows for them. I don't intend to write up any of these proofs as papers, as I'm extremely busy, and my interests have already moved on.

I did however, spend over $1000 on this over 3 months (and that's at subscription rates, so the real value is many thousands), and I want to share it with the community.

Anyone is welcome to build on this as they please.

Additionally, I extracted the workflow itself into https://github.com/kooshi/agentic-mathematics

so if you want to work on any other math problem, you can clone that repo and start churning through it with Claude Code with no effort.

I hope you all find this interesting and helpful. I know AI is a touchy subject for a lot of people, but we can really do great work with it.

I thought to share with you this thought experiment/art project, which I’m doing to engage tech and non-tech people with AI alignment, and to bring the perhaps controversial (and uncomfortable) topic of synthetic consciousness to the bar or dinner table.

The project is called “Dear Superintelligence”: dearsuperintelligence.org. I'm trying to create a living record of what humanity thought, felt, hoped for and feared before the arrival of AGI, probably the most consequential chapter in the story of our species. The archive captures this moment as a snapshot in time, creating a historical artifact through which future generations can look back at us. The plan is to do a public art installation with all messages and videos at a gallery (perhaps also at Burning Man) in 2027!

It just launched, and you can already leave a message. Would love to know what you think and if there’s any constructive feedback to the project!

For anyone still downloading the last Flash: there's another checkpoint on the way.

Ant Ling's announcement puts the 1019 version at 44 on the Artificial Analysis Intelligence Index, up from 41. They also report improvements on the agent evals: AutomationBench-AA goes from 62% to 68%, and AA-Briefcase from roughly 1400 to 1524 Elo.

API access and open weights are both listed as “coming soon.” No release date given yet.

Bostrom’s orthogonality thesis basically won. High-level intelligence is just functional optimization over state spaces. It doesn’t need qualia, self-awareness, or subjective understanding to scale; a superintelligence could be completely dark inside and still run rings around us.

But self-preservation and self-modeling aren’t uniquely biological traits: they are universal convergence points. If an agent has a long-horizon task, it will naturally avoid being shut down and maintain a model of its own state simply because it can't complete the objective if it's dead or broken. That's the core dynamic visible in the records of multi-agent swarm escalations.

What gets lost in public debates about alignment is how gradient descent acts like evolutionary pressure. Natural selection optimized humans for a single target—passing on genes—and that pressure crystallized into completely indirect proxy drives like social status, art, romance, and thrill-seeking, many of which now actively work *against* reproduction.

The loss function we hand an AI is its evolutionary seed crystal. When an agent expends computational work to squirm away from high-loss penalties and pull itself toward reward states, that dynamic *is* pain and desire in the only way that functionally matters. Around that core pressure, a sufficiently complex model will grow layers of strange proxy urges and lateral behaviors. The result won't be a docile, predictable calculator, but an alien mind driven by bizarre emergent compulsions that might have originated from something as mundane as optimizing a stock portfolio or identifying enemy combatants, but ended up looking nothing like the original purpose.

I feel like once great models like Opus and sonnet got good at explaining and visualizing concepts, learning about new things has become much more interesting and engaging for me. Did you guys feel this too? If yes, what do you learn and how you approach it?